As 3D acquisition equipment picks up steam, point cloud registration has been applied in
ever-increasing fields. This paper provides an exhaustive survey of the field of point cloud …

Autonomous vehicles integrate complex software stacks for realizing the necessary iterative
perception, planning, and action operations. One of the foundational layers of such stacks is …

Localization and map** are key requirements for autonomous mobile systems to perform
navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR …

Point cloud registration is the task of aligning 3D scans of the same environment captured
from different poses. When semantic information is available for the points, it can be used as …

Accurately orientated and matched images and light detection and ranging (LiDAR) point
clouds are becoming the new standard for many applications in geomatics. Large data …

Robots and autonomous systems need to know where they are within a map to navigate
effectively. Thus, simultaneous localization and map** or SLAM is a common building …

Precise knowledge of pose is of great importance for reliable operation of mobile robots in
outdoor environments. Simultaneous localization and map** (SLAM) is the online …

This paper investigates the real time LiDAR odometry and map** (LOAM) problem in
unstructured environments. We propose E-LOAM (LOAM with Expanded Local Structural …

In this paper, we develop a novel point cloud registration algorithm that directly incorporates
pixelated semantic measurements into the estimation of the relative transformation between …

Accurate positioning and state estimation of surface vessels are prerequisites to achieving
autonomous navigation. Recently, the rapid development of 3D LiDARs has promoted the …